# Set the working directory to the project folder. 
project_folder <- "."
setwd(project_folder)
library(Seurat)
library(ggplot2)
library(ggalluvial)
library(grid)
library(forcats)
library(see)
library(dplyr)
library(gghalves)
library(viridis)
library(stringr)
library(RColorBrewer)
library(kableExtra)
library(ggpubr)
library(rstatix)
library(clusterProfiler)
library(EnhancedVolcano)
library(introdataviz)
cols1 <- c(`0 CD8+ Eff mem (EM)` = "#A6CEE3", `1 CD8+ Eff cytotox (Ecyt)` = "#1F78B4",
    `3 CD4+ Naive/SCM` = "#33A02C", `6 CD4+ Central/Effector memory (CM/EM)` = "#FDBF6F",
    `9 γδ Tcells` = "#6A3D9A", `Proliferative cells` = "#F7D764")
cols2 <- c(`0 CD8+ Eff mem (EM)` = "#A6CEE3", `1 CD8+ Eff cytotox (Ecyt)` = "#1F78B4",
    `2 Early prolif: HMGN+/HMGB+/PCNA+ cells` = "#B2DF8A", `3 CD4+ Naive/SCM` = "#33A02C",
    `4 Early  prolif: MCM3/5/7+ PCNA+ cells` = "#FB9A99", `5 Late prolif: histones enriched MKI67+ cells` = "#E31A1C",
    `6 CD4+ Central/Effector memory (CM/EM)` = "#FDBF6F", `7 Ribosomal/Mitocondrial/Degradated cells` = "#FF7F00",
    `8 Late prolif: CDK+/CDC+/AURKA+ MIK67+ cells` = "#CAB2D6", `9 γδ Tcells` = "#6A3D9A")
cols3 <- c(`CAR+` = "#66c2a5", `CAR-` = "#fc8d62")
cols4 <- c("#264653", "#2a9d8f", "#e9c46a", "#f4a261", "#e76f51")
cols5 <- c(IP = "#4E6AAB", Peak = "#e78ac3")
cols6 <- c(CD4 = "#147D2C", CD8 = "#F5C936", Unknown = "#7f7f7f", `CD4- CD8-` = "#38369A")
cols7 <- c("#F8766D", "#00BA38", "#619CFF")
cols8 <- c("#E69F00FF", "#56B4E9FF", "#009E73FF", "#F0E442FF")

Activation

integrated.obj <- readRDS("integrated.obj.rds")

Analysis.

Define genes to analyse.

activation_genes <- c("CD28", "CD40LG", "TNFRSF4", "TNFRSF9", "CD74", "HLA-DBR1",
    "NKB1", "ICOS", "CD27", "CD25", "CD69")

integrated.obj <- AddModuleScore(object = integrated.obj, features = list(activation_genes),
    name = "Act.MGM")
## Warning: The following features are not present in the object: HLA-DBR1, NKB1,
## CD25, not searching for symbol synonyms
FeaturePlot(integrated.obj, features = activation_genes)
## Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features),
## : The following requested variables were not found: HLA-DBR1, NKB1, CD25

IP

integrated.obj.Peak <- subset(x = integrated.obj, subset = Timepoint == "IP")

# Signature expression

aux_df1 <- data.frame(seurat_clusters = Idents(integrated.obj.Peak), integrated.obj.Peak[[]],
    FetchData(integrated.obj.Peak, vars = activation_genes, slot = "data"))
## Warning in FetchData.Seurat(integrated.obj.Peak, vars = activation_genes, : The
## following requested variables were not found: HLA-DBR1, NKB1, CD25

Print the average, max and sd for each gene

aux_df1 %>%
    group_by(Patient_id, Timepoint, Class1) %>%
    summarise_at(vars(one_of(activation_genes)), list(mean = mean, max = max, sd = sd)) %>%
    kbl() %>%
    kable_styling(bootstrap_options = c("striped", "hover")) %>%
    scroll_box(width = "100%", height = "500px")
## Warning: Unknown columns: `HLA-DBR1`, `NKB1`, `CD25`
Patient_id Timepoint Class1 CD28_mean CD40LG_mean TNFRSF4_mean TNFRSF9_mean CD74_mean ICOS_mean CD27_mean CD69_mean CD28_max CD40LG_max TNFRSF4_max TNFRSF9_max CD74_max ICOS_max CD27_max CD69_max CD28_sd CD40LG_sd TNFRSF4_sd TNFRSF9_sd CD74_sd ICOS_sd CD27_sd CD69_sd
patient1 IP CAR+ 0.2032246 0.4474325 0.0151699 0.0132654 3.649087 0.1740156 0.9782899 0.7893528 2.742635 3.831090 1.664776 2.773125 5.894663 2.153638 3.294562 3.061552 0.4687367 0.7957521 0.1288320 0.1406676 0.7928318 0.4120311 0.8633980 0.8147787
patient1 IP CAR- 0.1819366 0.2889402 0.0060565 0.0081733 3.772949 0.1671164 1.0449855 0.7851441 2.507998 3.605574 1.868636 2.704251 5.624653 2.343362 3.514258 3.982876 0.4684201 0.6742635 0.0944544 0.1043026 0.6945903 0.4268390 0.9198547 0.8596306
patient2 IP CAR+ 0.2794793 0.2681035 0.0269683 0.0245779 4.549719 0.1626954 0.1689869 0.8437777 3.503515 3.597722 2.314096 2.580975 6.459877 2.805783 3.004467 4.240553 0.6717792 0.6849205 0.2106883 0.2065098 0.8538531 0.5207312 0.5504364 1.1184955
patient2 IP CAR- 0.2202422 0.1845875 0.0087081 0.0264293 3.857272 0.1509738 0.1669816 1.3033588 2.938584 3.251708 2.396987 2.339117 5.719099 2.830207 3.017315 4.751086 0.6291649 0.5975152 0.1333319 0.2138125 0.9632451 0.5130267 0.5849974 1.2823109
patient3 IP CAR+ 0.2334054 0.4018521 0.0672190 0.0211645 3.023431 0.1776469 0.6213310 0.7513492 3.232025 3.539359 2.632121 2.586069 5.681022 2.471989 3.173179 3.707736 0.5828129 0.7832169 0.3106180 0.1792936 1.0160658 0.4970965 0.8867879 0.9549400
patient3 IP CAR- 0.1673013 0.2368321 0.0222979 0.0359687 2.848937 0.1880342 0.6218271 0.8341690 3.413882 3.476551 2.277941 2.944534 5.264283 2.594053 3.297379 3.719444 0.5278786 0.6522536 0.1966603 0.2561078 1.1008412 0.5276756 0.9371440 1.0313439
patient4 IP CAR+ 0.1036454 0.4108187 0.0242136 0.0192158 3.465074 0.1289096 0.8560974 1.1911339 2.782725 3.332797 2.034856 2.405659 5.683235 2.878519 3.354256 3.443542 0.3873189 0.7427409 0.1811134 0.1622388 0.8899900 0.4127893 0.9212579 0.9617711
patient4 IP CAR- 0.0790173 0.1842659 0.0067281 0.0475107 3.905367 0.1155074 0.7206828 1.2312237 3.293625 3.209271 2.334051 3.383802 5.901725 2.999922 3.661372 3.844274 0.3675451 0.5601953 0.1082358 0.2863148 1.0704705 0.4215263 1.0037946 1.0808588
patient5 IP CAR+ 0.3419305 0.4275277 0.0019027 0.0170120 3.809501 0.3595868 0.5947328 0.6664258 3.216728 4.195087 1.660966 2.419071 5.932217 3.522941 3.491865 4.406818 0.7164245 0.8525841 0.0471570 0.1624464 0.9630692 0.7347437 0.9409721 0.9915923
patient5 IP CAR- 0.2792884 0.4001225 0.0000000 0.0288433 3.662810 0.2951297 0.8958281 0.6999004 3.226201 3.230468 0.000000 2.316564 5.610964 2.924298 3.566537 4.033406 0.6442354 0.8075789 0.0000000 0.2035010 0.9114614 0.6435492 1.0453935 0.9706036 
FeaturePlot(integrated.obj, features = "Act.MGM1")

# CAR+/CAR-

ggplot(aux_df1, aes(x = fct_reorder(Class1, Act.MGM1, .fun = median, .desc = TRUE),
    y = Act.MGM1, fill = Class1)) + geom_violin(scale = "width") + geom_boxplot(outlier.shape = NA,
    width = 0.1) + theme_classic() + theme(axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16)) + theme(axis.text.x = element_text(angle = 90,
    vjust = 0.5, hjust = 1)) + xlab("") + scale_fill_manual(values = cols3)

# CD4/CD8
aux_df1 <- aux_df1[which(aux_df1$final_criteria != "Unknown"), ]

ggplot(aux_df1, aes(x = fct_reorder(final_criteria, Act.MGM1, .fun = median, .desc = TRUE),
    y = Act.MGM1, fill = final_criteria)) + geom_violin(scale = "width") + geom_boxplot(outlier.shape = NA,
    width = 0.1) + theme_classic() + theme(axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16)) + theme(axis.text.x = element_text(angle = 90,
    vjust = 0.5, hjust = 1)) + xlab("") + scale_fill_manual(values = cols6)

ggplot(aux_df1, aes(x = fct_reorder(Class1, Act.MGM1, .fun = median, .desc = TRUE),
    y = Act.MGM1, fill = final_criteria)) + geom_split_violin() + geom_boxplot(outlier.shape = NA,
    width = 0.1) + theme_classic() + theme(axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16)) + theme(axis.text.x = element_text(angle = 90,
    vjust = 0.5, hjust = 1)) + xlab("") + scale_fill_manual(values = cols6)

Peak

integrated.obj.Peak <- subset(x = integrated.obj, subset = Timepoint == "Peak")

aux_df1 <- data.frame(seurat_clusters = Idents(integrated.obj.Peak), integrated.obj.Peak[[]],
    FetchData(integrated.obj.Peak, vars = activation_genes, slot = "data"))
## Warning in FetchData.Seurat(integrated.obj.Peak, vars = activation_genes, : The
## following requested variables were not found: HLA-DBR1, NKB1, CD25

Print the average, max and sd for each gene

aux_df1 %>%
    group_by(Patient_id, Timepoint, Class1) %>%
    summarise_at(vars(one_of(activation_genes)), list(mean = mean, max = max, sd = sd)) %>%
    kbl() %>%
    kable_styling(bootstrap_options = c("striped", "hover")) %>%
    scroll_box(width = "100%", height = "500px")
## Warning: Unknown columns: `HLA-DBR1`, `NKB1`, `CD25`
Patient_id Timepoint Class1 CD28_mean CD40LG_mean TNFRSF4_mean TNFRSF9_mean CD74_mean ICOS_mean CD27_mean CD69_mean CD28_max CD40LG_max TNFRSF4_max TNFRSF9_max CD74_max ICOS_max CD27_max CD69_max CD28_sd CD40LG_sd TNFRSF4_sd TNFRSF9_sd CD74_sd ICOS_sd CD27_sd CD69_sd
patient1 Peak CAR+ 0.3627902 0.0239788 0.0008582 0.2607525 4.203406 0.0622529 1.9005337 0.9428480 2.950523 3.404457 1.957659 3.302408 6.358915 2.488029 4.330509 4.041383 0.7211257 0.2178625 0.0409897 0.6334359 0.7651602 0.3150630 1.1067263 1.044877
patient1 Peak CAR- 0.1913131 0.0703242 0.0000000 0.0673668 3.594059 0.0593826 0.7344711 1.0785020 2.891553 2.784861 0.000000 2.947666 6.724743 2.620819 3.796418 3.785936 0.5414251 0.3468586 0.0000000 0.3387592 1.0793991 0.3139337 1.1058965 1.089465
patient2 Peak CAR+ 0.1649673 0.0373742 0.0000000 0.0440195 3.488778 0.0879091 0.5882039 1.2325634 3.088088 3.157958 0.000000 2.832461 5.603116 2.394267 3.516896 3.935099 0.5611679 0.2840945 0.0000000 0.3075817 0.9824115 0.3999162 1.0255506 1.249697
patient2 Peak CAR- 0.0729409 0.0325194 0.0200617 0.0205974 3.454619 0.0685784 0.2450133 1.3602044 2.484494 2.683933 2.600378 2.411182 5.454322 2.720268 4.281721 4.078757 0.3391582 0.2414754 0.1957288 0.1931668 1.0083317 0.3310355 0.6681393 1.211465
patient3 Peak CAR+ 0.3627520 0.0322195 0.0000000 0.0646778 2.812122 0.1120834 0.9173257 1.5548438 3.298215 2.729210 0.000000 2.936892 5.073486 3.057955 3.666001 4.464503 0.8264029 0.2587836 0.0000000 0.3678444 1.3332435 0.4707304 1.1765913 1.365613
patient3 Peak CAR- 0.2016444 0.0803131 0.0077465 0.0154524 2.563344 0.1173346 0.7340135 1.3066376 2.890750 2.833213 2.205085 2.956552 5.280605 3.441517 3.687588 4.158982 0.5922904 0.3821949 0.1198768 0.1811185 1.3257329 0.4633052 1.0448009 1.204572
patient4 Peak CAR+ 0.1943009 0.0493635 0.0000000 0.1480889 3.993479 0.0917020 1.2380745 1.0357098 2.840016 2.995400 0.000000 2.797789 5.596637 3.341898 3.759834 3.731695 0.5474784 0.3157714 0.0000000 0.4768167 0.8868135 0.4063849 1.1300259 1.084852
patient4 Peak CAR- 0.1224278 0.0802020 0.0013005 0.0108890 3.574894 0.1193289 0.2880645 0.9882553 3.029411 3.262917 2.326511 2.850702 5.418546 3.029411 3.593488 3.789069 0.4674077 0.3973381 0.0550047 0.1419216 1.1215928 0.4682750 0.7435651 1.162460
patient5 Peak CAR+ 0.1489820 0.0296727 0.0000000 0.0648870 3.279515 0.0968585 1.5172309 1.0504938 2.823479 2.849934 0.000000 2.896389 5.075855 2.253173 3.899294 3.997759 0.4853103 0.2435035 0.0000000 0.3132820 0.9454487 0.3746767 1.0952072 1.072427
patient5 Peak CAR- 0.1148940 0.0582123 0.0000000 0.0472712 2.927868 0.0795384 1.1184764 1.2214102 3.046429 2.672986 0.000000 2.844580 5.379268 2.709296 4.177422 3.976987 0.4348365 0.3198854 0.0000000 0.2940132 1.1507081 0.3543391 1.1572611 1.142109 
FeaturePlot(integrated.obj, features = "Act.MGM1")

# CAR+/CAR-

ggplot(aux_df1, aes(x = fct_reorder(Class1, Act.MGM1, .fun = median, .desc = TRUE),
    y = Act.MGM1, fill = Class1)) + geom_violin(scale = "width") + geom_boxplot(outlier.shape = NA,
    width = 0.1) + theme_classic() + theme(axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16)) + theme(axis.text.x = element_text(angle = 90,
    vjust = 0.5, hjust = 1)) + xlab("") + scale_fill_manual(values = cols3)

# CD4/CD8
aux_df1 <- aux_df1[which(aux_df1$final_criteria != "Unknown"), ]

ggplot(aux_df1, aes(x = fct_reorder(final_criteria, Act.MGM1, .fun = median, .desc = TRUE),
    y = Act.MGM1, fill = final_criteria)) + geom_violin(scale = "width") + geom_boxplot(outlier.shape = NA,
    width = 0.1) + theme_classic() + theme(axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16)) + theme(axis.text.x = element_text(angle = 90,
    vjust = 0.5, hjust = 1)) + xlab("") + scale_fill_manual(values = cols6)

CAR+

integrated.obj.Peak <- subset(x = integrated.obj, subset = Class1 == "CAR+")


aux_df1 <- data.frame(seurat_clusters = Idents(integrated.obj.Peak), integrated.obj.Peak[[]],
    FetchData(integrated.obj.Peak, vars = activation_genes, slot = "data"))
## Warning in FetchData.Seurat(integrated.obj.Peak, vars = activation_genes, : The
## following requested variables were not found: HLA-DBR1, NKB1, CD25

Print the average, max and sd for each gene

aux_df1 %>%
    group_by(Patient_id, Timepoint, Class1) %>%
    summarise_at(vars(one_of(activation_genes)), list(mean = mean, max = max, sd = sd)) %>%
    kbl() %>%
    kable_styling(bootstrap_options = c("striped", "hover")) %>%
    scroll_box(width = "100%", height = "500px")
## Warning: Unknown columns: `HLA-DBR1`, `NKB1`, `CD25`
Patient_id Timepoint Class1 CD28_mean CD40LG_mean TNFRSF4_mean TNFRSF9_mean CD74_mean ICOS_mean CD27_mean CD69_mean CD28_max CD40LG_max TNFRSF4_max TNFRSF9_max CD74_max ICOS_max CD27_max CD69_max CD28_sd CD40LG_sd TNFRSF4_sd TNFRSF9_sd CD74_sd ICOS_sd CD27_sd CD69_sd
patient1 IP CAR+ 0.2032246 0.4474325 0.0151699 0.0132654 3.649087 0.1740156 0.9782899 0.7893528 2.742635 3.831090 1.664776 2.773125 5.894663 2.153638 3.294562 3.061552 0.4687367 0.7957521 0.1288320 0.1406676 0.7928318 0.4120311 0.8633980 0.8147787
patient1 Peak CAR+ 0.3627902 0.0239788 0.0008582 0.2607525 4.203406 0.0622529 1.9005337 0.9428480 2.950523 3.404457 1.957659 3.302408 6.358915 2.488029 4.330509 4.041383 0.7211257 0.2178625 0.0409897 0.6334359 0.7651602 0.3150630 1.1067263 1.0448767
patient2 IP CAR+ 0.2794793 0.2681035 0.0269683 0.0245779 4.549719 0.1626954 0.1689869 0.8437777 3.503515 3.597722 2.314096 2.580975 6.459877 2.805783 3.004467 4.240553 0.6717792 0.6849205 0.2106883 0.2065098 0.8538531 0.5207312 0.5504364 1.1184955
patient2 Peak CAR+ 0.1649673 0.0373742 0.0000000 0.0440195 3.488778 0.0879091 0.5882039 1.2325634 3.088088 3.157958 0.000000 2.832461 5.603116 2.394267 3.516896 3.935099 0.5611679 0.2840945 0.0000000 0.3075817 0.9824115 0.3999162 1.0255506 1.2496967
patient3 IP CAR+ 0.2334054 0.4018521 0.0672190 0.0211645 3.023431 0.1776469 0.6213310 0.7513492 3.232025 3.539359 2.632121 2.586069 5.681022 2.471989 3.173179 3.707736 0.5828129 0.7832169 0.3106180 0.1792936 1.0160658 0.4970965 0.8867879 0.9549400
patient3 Peak CAR+ 0.3627520 0.0322195 0.0000000 0.0646778 2.812122 0.1120834 0.9173257 1.5548438 3.298215 2.729210 0.000000 2.936892 5.073486 3.057955 3.666001 4.464503 0.8264029 0.2587836 0.0000000 0.3678444 1.3332435 0.4707304 1.1765913 1.3656132
patient4 IP CAR+ 0.1036454 0.4108187 0.0242136 0.0192158 3.465074 0.1289096 0.8560974 1.1911339 2.782725 3.332797 2.034856 2.405659 5.683235 2.878519 3.354256 3.443542 0.3873189 0.7427409 0.1811134 0.1622388 0.8899900 0.4127893 0.9212579 0.9617711
patient4 Peak CAR+ 0.1943009 0.0493635 0.0000000 0.1480889 3.993479 0.0917020 1.2380745 1.0357098 2.840016 2.995400 0.000000 2.797789 5.596637 3.341898 3.759834 3.731695 0.5474784 0.3157714 0.0000000 0.4768167 0.8868135 0.4063849 1.1300259 1.0848520
patient5 IP CAR+ 0.3419305 0.4275277 0.0019027 0.0170120 3.809501 0.3595868 0.5947328 0.6664258 3.216728 4.195087 1.660966 2.419071 5.932217 3.522941 3.491865 4.406818 0.7164245 0.8525841 0.0471570 0.1624464 0.9630692 0.7347437 0.9409721 0.9915923
patient5 Peak CAR+ 0.1489820 0.0296727 0.0000000 0.0648870 3.279515 0.0968585 1.5172309 1.0504938 2.823479 2.849934 0.000000 2.896389 5.075855 2.253173 3.899294 3.997759 0.4853103 0.2435035 0.0000000 0.3132820 0.9454487 0.3746767 1.0952072 1.0724268 
FeaturePlot(integrated.obj, features = "Act.MGM1")

# IP/Peak

ggplot(aux_df1, aes(x = fct_reorder(Timepoint, Act.MGM1, .fun = median, .desc = TRUE),
    y = Act.MGM1, fill = Timepoint)) + geom_violin(scale = "width") + geom_boxplot(outlier.shape = NA,
    width = 0.1) + theme_classic() + theme(axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16)) + theme(axis.text.x = element_text(angle = 90,
    vjust = 0.5, hjust = 1)) + xlab("") + scale_fill_manual(values = cols5)

# CD4/CD8
aux_df1 <- aux_df1[which(aux_df1$final_criteria != "Unknown"), ]

ggplot(aux_df1, aes(x = fct_reorder(final_criteria, Act.MGM1, .fun = median, .desc = TRUE),
    y = Act.MGM1, fill = final_criteria)) + geom_violin(scale = "width") + geom_boxplot(outlier.shape = NA,
    width = 0.1) + theme_classic() + theme(axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16)) + theme(axis.text.x = element_text(angle = 90,
    vjust = 0.5, hjust = 1)) + xlab("") + scale_fill_manual(values = cols6)

Session Info

sessionInfo()
## R version 4.3.0 (2023-04-21)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Madrid
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] introdataviz_0.0.0.9003 EnhancedVolcano_1.18.0  ggrepel_0.9.3          
##  [4] clusterProfiler_4.8.1   rstatix_0.7.2           ggpubr_0.6.0           
##  [7] kableExtra_1.3.4        RColorBrewer_1.1-3      stringr_1.5.0          
## [10] viridis_0.6.3           viridisLite_0.4.2       gghalves_0.1.4         
## [13] dplyr_1.1.2             see_0.7.5               forcats_1.0.0          
## [16] ggalluvial_0.12.5       ggplot2_3.4.2           SeuratObject_4.1.3     
## [19] Seurat_4.3.0            knitr_1.43             
## 
## loaded via a namespace (and not attached):
##   [1] RcppAnnoy_0.0.20        splines_4.3.0           later_1.3.1            
##   [4] ggplotify_0.1.0         bitops_1.0-7            tibble_3.2.1           
##   [7] polyclip_1.10-4         lifecycle_1.0.3         globals_0.16.2         
##  [10] lattice_0.21-8          MASS_7.3-60             backports_1.4.1        
##  [13] magrittr_2.0.3          plotly_4.10.1           sass_0.4.6             
##  [16] rmarkdown_2.21          jquerylib_0.1.4         yaml_2.3.7             
##  [19] httpuv_1.6.11           sctransform_0.3.5       sp_1.6-0               
##  [22] spatstat.sparse_3.0-1   reticulate_1.28         cowplot_1.1.1          
##  [25] pbapply_1.7-0           DBI_1.1.3               abind_1.4-5            
##  [28] zlibbioc_1.46.0         rvest_1.0.3             Rtsne_0.16             
##  [31] purrr_1.0.1             ggraph_2.1.0            BiocGenerics_0.46.0    
##  [34] RCurl_1.98-1.12         yulab.utils_0.0.6       tweenr_2.0.2           
##  [37] GenomeInfoDbData_1.2.10 enrichplot_1.20.0       IRanges_2.34.0         
##  [40] S4Vectors_0.38.1        irlba_2.3.5.1           listenv_0.9.0          
##  [43] spatstat.utils_3.0-3    tidytree_0.4.2          goftest_1.2-3          
##  [46] spatstat.random_3.1-5   fitdistrplus_1.1-11     parallelly_1.35.0      
##  [49] svglite_2.1.1           leiden_0.4.3            codetools_0.2-19       
##  [52] ggforce_0.4.1           DOSE_3.26.1             xml2_1.3.4             
##  [55] tidyselect_1.2.0        aplot_0.1.10            farver_2.1.1           
##  [58] matrixStats_0.63.0      stats4_4.3.0            spatstat.explore_3.2-1 
##  [61] webshot_0.5.4           jsonlite_1.8.4          tidygraph_1.2.3        
##  [64] ellipsis_0.3.2          progressr_0.13.0        ggridges_0.5.4         
##  [67] survival_3.5-5          systemfonts_1.0.4       tools_4.3.0            
##  [70] treeio_1.24.0           ica_1.0-3               Rcpp_1.0.10            
##  [73] glue_1.6.2              gridExtra_2.3           xfun_0.39              
##  [76] qvalue_2.32.0           GenomeInfoDb_1.36.0     withr_2.5.0            
##  [79] formatR_1.14            fastmap_1.1.1           fansi_1.0.4            
##  [82] digest_0.6.31           gridGraphics_0.5-1      R6_2.5.1               
##  [85] mime_0.12               colorspace_2.1-0        GO.db_3.17.0           
##  [88] scattermore_1.1         tensor_1.5              spatstat.data_3.0-1    
##  [91] RSQLite_2.3.1           utf8_1.2.3              tidyr_1.3.0            
##  [94] generics_0.1.3          data.table_1.14.8       graphlayouts_1.0.0     
##  [97] httr_1.4.6              htmlwidgets_1.6.2       scatterpie_0.2.0       
## [100] uwot_0.1.14             pkgconfig_2.0.3         gtable_0.3.3           
## [103] blob_1.2.4              lmtest_0.9-40           XVector_0.40.0         
## [106] shadowtext_0.1.2        htmltools_0.5.5         carData_3.0-5          
## [109] fgsea_1.26.0            scales_1.2.1            Biobase_2.60.0         
## [112] png_0.1-8               ggfun_0.0.9             rstudioapi_0.14        
## [115] reshape2_1.4.4          nlme_3.1-162            cachem_1.0.8           
## [118] zoo_1.8-12              KernSmooth_2.23-21      HDO.db_0.99.1          
## [121] parallel_4.3.0          miniUI_0.1.1.1          AnnotationDbi_1.62.1   
## [124] pillar_1.9.0            vctrs_0.6.2             RANN_2.6.1             
## [127] promises_1.2.0.1        car_3.1-2               xtable_1.8-4           
## [130] cluster_2.1.4           evaluate_0.21           cli_3.6.1              
## [133] compiler_4.3.0          rlang_1.1.1             crayon_1.5.2           
## [136] future.apply_1.11.0     ggsignif_0.6.4          labeling_0.4.2         
## [139] plyr_1.8.8              stringi_1.7.12          deldir_1.0-6           
## [142] BiocParallel_1.34.2     munsell_0.5.0           Biostrings_2.68.1      
## [145] lazyeval_0.2.2          spatstat.geom_3.2-1     GOSemSim_2.26.0        
## [148] Matrix_1.5-4            patchwork_1.1.2         bit64_4.0.5            
## [151] future_1.32.0           KEGGREST_1.40.0         shiny_1.7.4            
## [154] highr_0.10              ROCR_1.0-11             igraph_1.4.2           
## [157] broom_1.0.4             memoise_2.0.1           bslib_0.4.2            
## [160] ggtree_3.8.0            fastmatch_1.1-3         bit_4.0.5              
## [163] downloader_0.4          gson_0.1.0              ape_5.7-1